2385 papers
This paper introduces TA-GGAD, a testing-time adaptive graph foundation model that addresses the cross-domain generalization challenge in anomaly detection by identifying and modeling the "Anomaly Disassortativity" issue, thereby achieving state-of-the-art performance across diverse real-world graphs with a single training phase.
This paper introduces a differentially private, zero-order optimization framework that extends dataset condensation to non-differentiable clinical models, enabling the creation of compact, privacy-preserving synthetic datasets that facilitate the democratization of clinical data sharing without compromising model utility.
This paper proposes M3GCLR, a game-theoretic contrastive learning framework that addresses limitations in existing skeleton-based action recognition methods by establishing an Infinite Skeleton-data Game model with a mini-max optimization strategy and dual-loss equilibrium optimizer to effectively handle view discrepancies, adversarial mechanisms, and augmentation perturbations, achieving state-of-the-art performance on multiple benchmarks.
The paper proposes MIL-PF, a scalable framework that combines frozen foundation model encoders with a lightweight attention-based Multiple Instance Learning head to achieve state-of-the-art mammography classification while significantly reducing computational costs and training complexity.
SPAARS is a curriculum learning framework for offline-to-online reinforcement learning that safely improves policies by initially exploring a low-dimensional latent space to ensure sample efficiency and stability, then seamlessly transitioning to raw action space to bypass decoder-induced performance ceilings, thereby achieving superior results over state-of-the-art baselines on both robotic manipulation and locomotion tasks.
This paper introduces the Pulse-Train-Resonator (PTR), a differentiable synthesis model that improves engine sound generation by directly modeling sequential exhaust pressure pulses and physical resonances rather than approximating spectral characteristics, achieving superior reconstruction accuracy and interpretability across diverse engine types.
This paper presents the ICDAR 2025 competition on end-to-end document image machine translation, detailing its dual-track structure for small and large models, participation statistics, and findings that highlight large-model approaches as a promising paradigm for handling complex document layouts.
This paper introduces the Fully Convolutional Diffusion Model (FCDM), a ConvNeXt-based architecture that achieves competitive generative performance with significantly fewer computational resources and training steps than Transformer-based counterparts, demonstrating that modern convolutional designs remain a highly efficient alternative for scaling diffusion models.
This paper introduces PromptDLA, a domain-aware framework that leverages descriptive knowledge as cues to customize prompts for integrating domain priors, thereby overcoming the limitations of directly merging diverse datasets and achieving state-of-the-art performance in Document Layout Analysis across multiple benchmarks.
This paper proposes a real-time multi-modal trajectory policy framework that distills a Conditional Flow Matching expert into a single-step student using Implicit Maximum Likelihood Estimation and a bi-directional Chamfer distance, thereby eliminating the latency of iterative ODE integration while preserving multi-modal action diversity for high-frequency robotic control.
This study investigates gender bias in Large Language Models within French healthcare contexts, demonstrating that these models rely on embedded stereotypes when processing interactions between gender and other social determinants of health, thereby highlighting the need for context-specific assessments that go beyond evaluating individual factors in isolation.
This paper introduces "Open-World Motion Forecasting," an end-to-end class-incremental framework that predicts future trajectories directly from camera images while mitigating catastrophic forgetting through pseudo-labeling with vision-language models and a novel query feature variance-based replay strategy, enabling continual adaptation to evolving object taxonomies in real-world autonomous driving.
CERES is a novel, open-access, and continuously running probabilistic forecasting system that fuses six global data streams to generate weekly, machine-readable 90-day ahead risk estimates for acute food insecurity across 43 high-risk countries, with a unique commitment to public prospective verification of all predictions.
This paper introduces the CoMoral benchmark to reveal that current Large Language Models exhibit a critical limitation where they prioritize moral reasoning over commonsense understanding, particularly struggling to detect contradictions involving primary characters due to a pervasive narrative focus bias.
This paper introduces an open, transparent, and reproducible dataset and methodology for evaluating NLP and RAG systems on EU AI Act compliance, featuring tasks like risk classification and obligation generation that leverage large language models to address regulatory ambiguities and achieve high performance scores.
This paper introduces OncoAgent, a novel guideline-aware AI agent that achieves zero-shot, training-free auto-delineation of clinical target volumes by converting textual clinical guidelines into 3D contours, demonstrating superior adaptability and physician preference over traditional supervised deep learning models.
This paper introduces Variational Mixture-of-Experts Routing (VMoER), a scalable Bayesian framework that confines uncertainty quantification to the expert-selection stage of Mixture-of-Experts Transformers, achieving significant improvements in calibration, stability, and out-of-distribution detection with negligible computational overhead.
This paper introduces RoadLogic, an open-source framework that bridges declarative OpenSCENARIO specifications and executable simulations by combining Answer Set Programming, motion planning, and specification-based monitoring to automatically generate diverse, realistic, and compliant autonomous vehicle testing scenarios.
This paper identifies and theoretically explains "task-level merging collapse," a phenomenon where incompatible task representations cause catastrophic performance degradation in merged LLMs, demonstrating that representational incompatibility—not parameter-space conflicts—is the primary driver of failure and establishing fundamental limits on task mergeability via rate-distortion theory.
EvoDriveVLA is a novel Vision-Language-Action model for autonomous driving that overcomes perception degradation and planning instability through a collaborative distillation framework combining self-anchored visual constraints and oracle-guided trajectory optimization to achieve state-of-the-art performance.